Digital Twin Execution Report

The Digital Twin(DT) Execution Report shows the results of a DT execution.

Preparation

Python libraries are imported, and historical and simulation data is imported for evaluation.

Dependecies

In [1]:
%load_ext autotime
time: 176 µs (started: 2021-11-10 16:04:02 -07:00)
In [2]:
import pandas as pd
import plotly.express as px
import json
import os
import plotly.offline as pyo
pyo.init_notebook_mode()
time: 809 ms (started: 2021-11-10 16:04:02 -07:00)

Load Data

In [3]:
base_path = "../../data/runs/2021-06-09 16:49:23.697639-"
time: 238 µs (started: 2021-11-10 16:04:04 -07:00)
In [4]:
# Parameters
base_path = "/Users/zhiweili/Box Sync/localproj/reflexer-digital-twin-master/data/runs/2021-11-10 23:03:34.639181-"
time: 237 µs (started: 2021-11-10 16:04:04 -07:00)
In [5]:
meta_path = base_path + "meta.json"
historical_path = base_path + "historical.csv.gz"
backtesting_path = base_path + "backtesting.csv.gz"
extrapolation_path = base_path + "extrapolation.csv.gz"
time: 383 µs (started: 2021-11-10 16:04:04 -07:00)
In [6]:
with open(meta_path, 'r') as fid:
    metadata = json.load(fid)
for i, row in metadata.items():
    print(f"{i}: {row}")
print("---")
createdAt: 2021-11-10 23:03:34.639181
initial_backtesting_timestamp: 2021-05-30 21:40:11
final_backtesting_timestamp: 2021-06-13 18:24:31
---
time: 1.48 ms (started: 2021-11-10 16:04:04 -07:00)
In [7]:
historical_df = pd.read_csv(historical_path).assign(origin='historical').iloc[1:].assign(subset=-1)
backtesting_df = pd.read_csv(backtesting_path).assign(origin='backtesting').iloc[1:]
extrapolation_df = pd.read_csv(extrapolation_path).assign(origin='extrapolation')

historical_df.loc[:, 'seconds_passed'] = backtesting_df.seconds_passed

past_df = (pd.concat([historical_df, backtesting_df])
             .reset_index(drop=True)
             .assign(seconds_passed=lambda df: df.seconds_passed - df.seconds_passed.min())          
          )

extrapolation_df.loc[:, 'seconds_passed'] += past_df.seconds_passed.max()
df = pd.concat([past_df, extrapolation_df])

df = (df
      .assign(hours_passed=lambda df: df.seconds_passed / (60 * 60))
      .assign(days_passed=lambda df: df.seconds_passed / (24 * 60 * 60))
     )

initial_time = pd.Timestamp(metadata['initial_backtesting_timestamp'])
deltas = df.hours_passed.map(lambda x: pd.Timedelta(x, unit='h'))
times = initial_time + deltas
df = df.assign(timestamp=times).reset_index()
last_time = df.query('origin == "extrapolation"').timestamp.min()

# Wrangling for extrapolation scenarios
def extrapolation_origin(_df):
    df = (_df.query("origin == 'extrapolation'")
             .assign(use_ewm_model=lambda df: df.use_ewm_model.fillna(0).astype(int))
             .assign(convergence_swap_intensity=lambda df: df.convergence_swap_intensity.fillna(0))
         )
    s = df.apply(lambda row: f"extrapolation (ewm={row.use_ewm_model}, csi={row.convergence_swap_intensity :.0%})", axis=1)
    return s

s = extrapolation_origin(df)
df.loc[s.index, 'origin'] = s
time: 37.3 ms (started: 2021-11-10 16:04:04 -07:00)

Visualizations

We will evaluate pricing feeds, the controller state, and the token state with visualizations.

Prices

In [8]:
value_cols = ('eth_price', 'market_price')
id_cols = {'timestamp', 'origin', 'subset'}
fig_df = (df.melt(id_vars=id_cols, value_vars=value_cols)
            .replace({'market_price': 'RAI Market Price (USD/RAI)'})
            .replace({'eth_price': 'ETH Price (USD/RAI)'})
         )
fig = px.line(fig_df,
              x='timestamp',
              y='value',
              color='origin',
              facet_row='variable',
              line_group='subset',
              labels={'market_price': 'RAI Market Price in USD'},
              title='Prices over time')
fig.update_traces(line=dict(width=0.5), 
                  marker=dict(opacity=0.05, size=5),
                  mode='lines+markers')
fig.add_vline(initial_time.timestamp() * 1000,
              annotation_text=initial_time.strftime('%Y-%m-%d %Hh'))
fig.add_vline(last_time.timestamp() * 1000,
              annotation_text=last_time.strftime('%Y-%m-%d %Hh'))
fig.add_vrect(x0=last_time, 
              x1=fig_df.timestamp.max(), 
              annotation_text="Extrapolation", 
              annotation_position="top right",
              fillcolor="cyan", 
              opacity=0.05)
fig.update_yaxes(matches=None)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.show()
time: 1.13 s (started: 2021-11-10 16:04:05 -07:00)

In the plots above we evaluate the Eth price data (upper) and the Rai market price data (lower) showing the historical data and the forward extrapolation under various assumptions Exponential Moving Average (EWM) and optimal swap assumptions. The CSI number indicating the maximum intensity of the 'optimal swap'.

Controller State

In [9]:
value_cols = ('redemption_price', 
              'redemption_rate', 
              'redemption_rate_annual',
              'proportional_error',
              'integral_error')
id_cols = {'timestamp', 'origin', 'subset'}

fig_df = (df.assign(redemption_rate_annual=lambda df: df.redemption_rate ** (24 * 365))
            .melt(id_vars=id_cols, value_vars=value_cols)
            .replace({'redemption_price': 'Redemption Price (USD/RAI)'})
            .replace({'redemption_rate': 'Redemption Rate (%/hours)'})
            .replace({'redemption_rate_annual': 'Redemption Rate (%/year)'})
            .replace({'proportional_error': 'Proportional Error (USD/RAI)'})
            .replace({'integral_error': 'Integral Error (USD * s / RAI)'})
         )
fig = px.line(fig_df,
              x='timestamp',
              y='value',
              color='origin',
              facet_row='variable',
              line_group='subset',
              height=1200,
              title='Controller State over time')
fig.update_traces(line=dict(width=0.5), 
                  marker=dict(opacity=0.05, size=5),
                  mode='lines+markers')
fig.add_vline(initial_time.timestamp() * 1000,
              annotation_text=initial_time.strftime('%Y-%m-%d %Hh'))
fig.add_vline(last_time.timestamp() * 1000,
              annotation_text=last_time.strftime('%Y-%m-%d %Hh'))
fig.add_vrect(x0=last_time, 
              x1=fig_df.timestamp.max(), 
              annotation_text="Extrapolation", 
              annotation_position="top right",
              fillcolor="cyan", 
              opacity=0.05)
fig.update_yaxes(matches=None)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.show()
time: 1.29 s (started: 2021-11-10 16:04:06 -07:00)

In the plots above we evaluate the controller states, in descending order(i.e. first state is the first plot) of redemption_price, redemption_rate, redemption_rate_annual,proportional_error, and integral_error. The plots show the historical data and the forward extrapolation under various assumptions Exponential Moving Average (EWM) and optimal swap assumptions. The CSI number indicating the maximum intensity of the 'optimal swap'.

Token State

In [10]:
value_cols = ('rai_debt', 'eth_locked', 'rai_reserve', 'eth_reserve')
id_cols = {'timestamp', 'origin', 'subset'}
fig_df = (df.melt(id_vars=id_cols, value_vars=value_cols)
            .replace({'rai_debt': 'Global RAI Debt'})
            .replace({'eth_locked': 'ETH Collateral'})
            .replace({'rai_reserve': 'RAI reserve on Uniswap'})
            .replace({'eth_reserve': 'ETH reserve on Uniswap'})
         )
fig = px.line(fig_df,
              x='timestamp',
              y='value',
              color='origin',
              facet_row='variable',
              line_group='subset',
              labels={'market_price': 'RAI Market Price in USD'},
              height=900,
              title='Token State')
fig.update_traces(line=dict(width=0.5), 
                  marker=dict(opacity=0.05, size=5),
                  mode='lines+markers')
fig.add_vline(initial_time.timestamp() * 1000,
              annotation_text=initial_time.strftime('%Y-%m-%d %Hh'))
fig.add_vline(last_time.timestamp() * 1000,
              annotation_text=last_time.strftime('%Y-%m-%d %Hh'))
fig.add_vrect(x0=last_time, 
              x1=fig_df.timestamp.max(), 
              annotation_text="Extrapolation", 
              annotation_position="top right",
              fillcolor="cyan", 
              opacity=0.05)
fig.update_yaxes(matches=None)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.show()
time: 980 ms (started: 2021-11-10 16:04:09 -07:00)

In the plots above we evaluate the token states, in descending order(i.e. first state is the first plot), of: rai_debt, eth_locked, rai_reserve, and eth_reserve. The plots show the historical data and the forward extrapolation under various assumptions Exponential Moving Average (EWM) and optimal swap assumptions. The CSI number indicating the maximum intensity of the 'optimal swap'.

Conclusion

In this report, we've shown the real historical data that flow into the DT, and then extrapolations for the potential values of these states.